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Accounting for Taste: Ranking Curators and Content in Social Networks

Published: 07 May 2016 Publication History

Abstract

Ranking users in social networks is a well-studied problem, typically solved by algorithms that leverage network structure to identify influential users and recommend people to follow. In the last decade, however, curation --- users sharing and promoting content in a network --- has become a central social activity, as platforms like Facebook, Twitter, Pinterest, and GitHub drive growth and engagement by connecting users through content and content to users. While existing algorithms reward users that are highly active with higher rankings, they fail to account for users' curatorial taste. This paper introduces CuRank, an algorithm for ranking users and content in social networks by explicitly modeling three characteristics of a good curator: discerning taste, high activity, and timeliness. We evaluate CuRank on datasets from two popular social networks --- GitHub and Vine --- and demonstrate its efficacy at ranking content and identifying good curators.

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  • (2016)Domain-Specific Recommendation by Matching Real Authors to Social Media UsersAdvances in Web-Based Learning – ICWL 201610.1007/978-3-319-47440-3_27(246-252)Online publication date: 1-Oct-2016

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  1. Accounting for Taste: Ranking Curators and Content in Social Networks

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    cover image ACM Conferences
    CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
    May 2016
    6108 pages
    ISBN:9781450333627
    DOI:10.1145/2858036
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 07 May 2016

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    Author Tags

    1. content
    2. curation
    3. ranking
    4. social networks

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    May 7 - 12, 2016
    California, San Jose, USA

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    • (2016)Domain-Specific Recommendation by Matching Real Authors to Social Media UsersAdvances in Web-Based Learning – ICWL 201610.1007/978-3-319-47440-3_27(246-252)Online publication date: 1-Oct-2016

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